Multi-resolution region-based clustering for urban analysis

International Journal of Remote Sensing

Volumn 31, Issue 22, 2010, Pages 5941-5973

  Kurtz, C ^a   Passat, N ^a   Gancarski P ^a   Puissant, A ^b  

^a UNIVERSITÉ DE STRASBOURG   (France)

^b UNIVERSITÉ DE STRASBOURG   (France)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RESOLUTION; MAPPING; SATELLITE IMAGERY; SEMANTICS;

CLUSTERING TECHNIQUES; FURTHER DEVELOPMENT; HETEROGENEOUS DATASETS; HIGH SPATIAL RESOLUTION; MULTI RESOLUTIONS; SATELLITE IMAGES; SEMANTIC LEVELS; SPATIAL RESOLUTION;

IMAGE SEGMENTATION;

CLUSTER ANALYSIS; MAPPING METHOD; SATELLITE DATA; SATELLITE IMAGERY; SEGMENTATION; SPATIAL RESOLUTION; URBAN PLANNING;

EID: 78649845453     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2010.512312     Document Type: Article

References (29)

1. Akcay, H. and Aksoy, S., 2008, Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Transactions on Geoscience and Remote Sensing, 46, pp. 2097-2111.
2. Baatz, M., Hoffmann, C. and Willhauck, G., 2008, Progressing from object-based to object-oriented image analysis. In Object-Based Image Analysis, W. Cartwright, G. Gartner, L. Meng, M.P. Peterson, T. Blaschke, S. Lang and G.J. Hay (Eds.), pp. 29-42 (Berlin: Springer).
3. Baatz, M. and Schape, A., 2000, Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In Proceedings of the Angewandte Geographische Informationsverarbeitung Symposium, Salzburg, Germany, pp. 12-23 (Karlsrune, Germany: Herbert Wichmann Verlag).
4. Barnsley, M. and Barr, S., 1997, Distinguishing urban land-use categories in fine spatial resolution land-cover data using a graph-based, structural pattern recognition system. Computers, Environment and Urban Systems, 21, pp. 209-225.
5. Bellman, R., 1961, Adaptive Control Processes (Princeton, NJ: Princeton University Press).
6. Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I. and Heynen, M., 2004, Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, pp. 239-258.
7. Blaschke, T., 2010, Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, pp. 2-16.
8. Bruzzone, L. and Carlin, V., 2006, A multilevel context-based system for classification of very high spatial resolution images. IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 2587-2600.
9. Caprioli, M. and Tarantino, E., 2003, Urban features recognition from VHR satellite data with an object-oriented approach. In Proceedings of the Commission IV Joint Workshop, Challenges in Geospatial Analysis, Integration and Visualization II, 8-9 September 2003, Stuttgart, Germany, pp. 8-9.
10. Carleer, A., Debeir, O. and Wolff, E., 2005, Assessment of very high spatial resolution satellite image segmentations. Photogrammetric Engineering and Remote Sensing, 71, pp. 1285-1294.
11. Carleer, A. and Wolff, E., 2006, Urban land cover multilevel region-based classification of VHR data by selecting relevant features. International Journal of Remote Sensing, 27, pp. 1035-1051.
12. Cha, S. and Srihari, S., 2002, On measuring the distance between histograms. Pattern Recognition, 35, pp. 1355-1370.
13. Chang, Y.L., Liang, L.S., Han, C.C., Fang, J.P., Liang, W.Y. and Chen, K.S., 2007, Multisource data fusion for landslide classification using generalized positive Boolean functions. IEEE Transactions on Geoscience and Remote Sensing, 45, pp. 1697-1708.
14. Chibani, Y., 2005, Selective synthetic aperture radar and panchromatic image fusion by using the á trous wavelet decomposition. EURASIP Journal on Applied Signal Processing, 2005, pp. 2207-2214.
15. Congalton, R., 1991, A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, pp. 35-46.
16. Corbane, C., Raclot, D., Jacob, F., Albergel, J. and Andrieux, P., 2008, Remote sensing of soil surface characteristics from a multiscale classification approach. CATENA, 75, pp. 308-318.
17. Cross, A., Mason, D. and Dury, S., 1988, Segmentation of remotely-sensed images by a split-and-merge process. International Journal of Remote Sensing, 9, pp. 1329-1345.
18. Forestier, G., Wemmert, C. and Gancarski, P., 2008, Multi-source images analysis using collaborative clustering. EURASIP Journal on Advances in Signal Processing, 2008, pp. 1-11.
19. Guo, D., Xiong, H. and Atluri, V., 2008, Object discovery in high-resolution remote sensing images: a semantic perspective. Knowledge and Information Systems, 19, pp. 211-233.
20. Herold, M., Liu, X. and Clarke, K., 2003, Spatial metrics and image texture for mapping urban land use. Photogrammetric Engineering and Remote Sensing, 69, pp. 991-1001.
21. Herold, M., Scepan, J., Muller, A. and Gunter, S., 2002, Object-oriented mapping and analysis of urban land use/cover using Ikonos data. In Proceedings of the Earsel Symposium Geoinformation for European-Wide Integration, 4-6 June 2002, Prague, Czech Republic, pp. 531-538.
22. Hughes, G., 1968, On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14, pp. 55-63.
23. Jacquin, A., Misakova, L. and Gay, M., 2008, A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landscape and Urban Planning, 84, pp. 152-165.
24. Marangoz, A., Oruc, M. and Buyuksalih, G., 2004, Object-oriented image analysis and semantic network for extracting the roads and buildings from ikonos pan-sharpened images. In Proceedings of the ISRPS Annual Conference, Istanbul, Turkey, pp. 19-23.
25. Meinel, G. and Neubert, M., 2004, A comparison of segmentation programs for high resolution remote sensing data. International Archives of Photogrammetry and Remote Sensing, XXXV, pp. 1097-1105.
26. Mesev, V., Gorte, B. and Longley, P., 2000, Modified maximum-likelihood classification algorithms and their application to urban remote sensing. In Remote Sensing and Urban Analysis, Vol. 1, pp. 71-89 (London, UK: Taylor & Francis).
27. Smeulders, A., Worring, M., Santini, S., Gupta, A. and Jain, R., 2000, Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, pp. 1349-1380.
28. Vincent, L. and Soille, P., 1991, Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, pp. 583-598.
29. Wemmert, C., Puissant, A., Forestier, G. and Ganç Arski, P., 2009, Multiresolution remote sensing image clustering. IEEE Geoscience and Remote Sensing Letters, 6, pp. 533-537.